Sentence Similarity
sentence-transformers
Safetensors
MLX
gemma3_text
text-embeddings-inference
feature-extraction
text-embeddings
turkish
tr
distillation
Instructions to use alibayram/embeddingmagibu-200m-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use alibayram/embeddingmagibu-200m-mlx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("alibayram/embeddingmagibu-200m-mlx") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - MLX
How to use alibayram/embeddingmagibu-200m-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir embeddingmagibu-200m-mlx alibayram/embeddingmagibu-200m-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
alibayram/embeddingmagibu-200m-mlx
The Model alibayram/embeddingmagibu-200m-mlx was converted to MLX format from alibayram/embeddingmagibu-200m using mlx-lm version 0.0.5.
Use with mlx
pip install mlx-embeddings
from mlx_embeddings import load, generate
import mlx.core as mx
model, tokenizer = load("alibayram/embeddingmagibu-200m-mlx")
# For text embeddings
output = generate(model, processor, texts=["I like grapes", "I like fruits"])
embeddings = output.text_embeds # Normalized embeddings
# Compute dot product between normalized embeddings
similarity_matrix = mx.matmul(embeddings, embeddings.T)
print("Similarity matrix between texts:")
print(similarity_matrix)
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Model size
32.4M params
Tensor type
F16
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U32 ·
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